Documents Josten, Cecily 3 results

"This study identifies the job attributes, and in particular skills and abilities, which predict the likelihood a job is recently automatable drawing on the Josten and Lordan (2020) classification of automatability, EU labour force survey data and a machine learning regression approach. We find that skills and abilities which relate to non-linear abstract thinking are those that are the safest from automation. We also find that jobs that require ‘people' engagement interacted with ‘brains' are also less likely to be automated. The skills that are required for these jobs include soft skills. Finally, we find that jobs that require physically making objects or physicality more generally are most likely to be automated unless they involve interaction with ‘brains' and/or ‘people'."

"This study builds on Autor and Dorn's (2013) classification of automatable work at the three-digit occupation code level to identify additional jobs that will be automatable in the next decade by drawing on patent data. Based on this new classification the study provides estimates of the share of jobs that we expect to be automatable in the EU and across 25 individual countries. The study highlights that aspects of 47% of jobs will be automatable over the next decade, with 35% of all jobs being fully automatable. It also provides some evidence that 'thinking' and 'people' skills will become increasingly important for the fourth industrial revolution. The study puts emphasis on the fact that these estimates are based on static models. Assuming that some of the rents from labor technology will filter back into the economy it is expected that other occupations will expand in number as people consume more goods and services."